TIRE cell input titration
Daniel Brown
2024-11-21
TIRE cell input titration
Aim
Investigate the dose response relationship of cell inputs to library size etc
Samples
A serial dilution will be prepared on the day of the experiment.
- Human HEK293T
- Mouse NIH3T3
- No template control
Standard TSO where UMI on TSO followed by spacer. Well barcode in line with read 1
Data to read in
I sequenced these libraries with the configuration
- Read 1 70bp
- Read 2 52bp
- Index read 1 and 2 8bp each
Check on plate map representation
Plot on plate map
All replicate dot plots by RNA amount
Library size log
plt1 <- ggplot(tb,
aes(x = Amount+1, y= sum+1, colour = Sample)) +
geom_point(size=2) +
ylab("Library Size (UMIs)") +
xlab("Cell equivalents") +
geom_smooth(method='lm', se = FALSE) +
scale_y_continuous(trans='log10') +
scale_x_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "l") +
annotation_logticks(base =10, sides = "b") +
scale_fill_brewer(palette = "Dark2") +
facet_wrap(~Replicate, ncol=1)
plt1
Library size in deduplicated UMIs
Show only 3rd replicate
Only replicate 1 HEK293T is problematic. However for the sake of clarity in publication I will 2nd replicate
tb_orig <- tb
tb <- tb %>%
filter(Replicate == "Two")
tb$Amount <- tb$Amount / 1000
tb$Amount <- as.factor(tb$Amount)Library size log
plt1 <- ggplot(tb,
aes(x = Amount, y= sum+1, colour = Sample)) +
geom_point(size=2) +
ylab("Library Size (UMIs)") +
xlab("Cell equivalents (000s)") +
geom_smooth(method='lm', se = FALSE) +
scale_y_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "l") +
scale_fill_brewer(palette = "Dark2")
plt1
Library size in deduplicated UMIs
Human and mouse mapping
Barnyard natural scale
p1 <- ggplot(tb, aes(x=subsets_Mouse_sum, y=subsets_Human_sum, colour=Sample)) +
geom_point(size=2) +
xlab("Mouse library size") +
ylab("Human library size") +
ylim(0,2e6) + xlim(0,2e6) +
scale_colour_brewer(palette = "Dark2")
p1
Barnyard log scale
- Looks deceptive and not how anyone else shows this.
- Replicate 2 nd 3 look very clean even in log representation
p2 <- ggplot(tb, aes(y=subsets_Human_sum, x=subsets_Mouse_sum, colour=Sample)) +
geom_point(size=2) +
xlab("Mouse library size") +
ylab("Human library size") +
scale_y_continuous(trans='log10') + scale_x_continuous(trans='log10') +
annotation_logticks(base = 10, sides = "bl") +
scale_colour_brewer(palette = "Dark2")
p2
Conclusion
- Replicate 1 looks compromised in terms of contamination especially one of the wells
- Replicate 1 HEK293T has contamination issues
- Otherwise all other wells within replicate 2 and replicate 3 have worked.
Session info
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Red Hat Enterprise Linux 9.4 (Plow)
##
## Matrix products: default
## BLAS: /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/lib64/R/lib/libRblas.so
## LAPACK: /stornext/System/data/software/rhel/9/base/tools/R/4.4.1/lib64/R/lib/libRlapack.so; LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Australia/Melbourne
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] ggthemes_5.1.0 here_1.0.1
## [3] patchwork_1.3.0 platetools_0.1.7
## [5] scater_1.32.1 scuttle_1.14.0
## [7] lubridate_1.9.3 forcats_1.0.0
## [9] stringr_1.5.1 dplyr_1.1.4
## [11] purrr_1.0.2 readr_2.1.5
## [13] tidyr_1.3.1 tibble_3.2.1
## [15] ggplot2_3.5.1 tidyverse_2.0.0
## [17] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
## [19] Biobase_2.64.0 GenomicRanges_1.56.2
## [21] GenomeInfoDb_1.40.1 IRanges_2.38.1
## [23] S4Vectors_0.42.1 BiocGenerics_0.50.0
## [25] MatrixGenerics_1.16.0 matrixStats_1.4.1
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.1 viridisLite_0.4.2
## [3] farver_2.1.2 vipor_0.4.7
## [5] viridis_0.6.5 fastmap_1.2.0
## [7] digest_0.6.37 rsvd_1.0.5
## [9] timechange_0.3.0 lifecycle_1.0.4
## [11] magrittr_2.0.3 compiler_4.4.1
## [13] rlang_1.1.4 sass_0.4.9
## [15] tools_4.4.1 utf8_1.2.4
## [17] yaml_2.3.10 knitr_1.48
## [19] labeling_0.4.3 S4Arrays_1.4.1
## [21] DelayedArray_0.30.1 RColorBrewer_1.1-3
## [23] abind_1.4-8 BiocParallel_1.38.0
## [25] withr_3.0.1 fansi_1.0.6
## [27] beachmat_2.20.0 colorspace_2.1-1
## [29] scales_1.3.0 cli_3.6.3
## [31] rmarkdown_2.28 crayon_1.5.3
## [33] generics_0.1.3 rstudioapi_0.17.0
## [35] httr_1.4.7 tzdb_0.4.0
## [37] DelayedMatrixStats_1.26.0 ggbeeswarm_0.7.2
## [39] cachem_1.1.0 splines_4.4.1
## [41] zlibbioc_1.50.0 parallel_4.4.1
## [43] XVector_0.44.0 vctrs_0.6.5
## [45] Matrix_1.7-0 jsonlite_1.8.9
## [47] BiocSingular_1.20.0 hms_1.1.3
## [49] BiocNeighbors_1.22.0 ggrepel_0.9.6
## [51] irlba_2.3.5.1 beeswarm_0.4.0
## [53] jquerylib_0.1.4 glue_1.8.0
## [55] codetools_0.2-20 stringi_1.8.4
## [57] gtable_0.3.5 UCSC.utils_1.0.0
## [59] ScaledMatrix_1.12.0 munsell_0.5.1
## [61] pillar_1.9.0 htmltools_0.5.8.1
## [63] GenomeInfoDbData_1.2.12 R6_2.5.1
## [65] sparseMatrixStats_1.16.0 rprojroot_2.0.4
## [67] evaluate_1.0.1 lattice_0.22-6
## [69] highr_0.11 bslib_0.8.0
## [71] Rcpp_1.0.13 nlme_3.1-164
## [73] gridExtra_2.3 SparseArray_1.4.8
## [75] mgcv_1.9-1 xfun_0.48
## [77] pkgconfig_2.0.3